Nowadays, automated conversational agents are deployed and integrated with online environments to interact with users. The automated conversational agents, such as chatbot or interactive voice response (IVR) system are deployed to assist the users in many ways, such as resolving concerns, making payments, raising inquiries, lodging complaints, and the like. Online environments may include web pages, widgets, links, gaming applications, mobile applications, etc. When an automated conversational agent, for example, a chatbot receives a message from a user, the chatbot determines intent of the message. The automated conversational agent then provides a response to the message based on the intent. The intent enables the automated conversational agent to understand the need of the user and provide a human-like response.
Typically, the intent may be determined using natural language understanding techniques. For example, the intent may be determined based on performing an intent classification using machine learning techniques. In the intent classification, each sentence in the message of the user may be considered as a data point. The data point may be manually annotated to label multiple intent categories. With due course of time, the automated conversational agent may encounter different sentences and learn many intents. The automated conversational agent may become more sophisticated and responsive based on the intents that are learned.
However, there may be a frequent need to update existing intents so that the responses of the automated conversational agent are accurate. There may be a need to add a new intent, split an existing intent into multiple intents or delete an intent. In order to frequently update the existing intents, a significant amount of manual effort may be required. For instance, adding a new intent may entail manually analyzing a huge amount of conversation dataset, which may not be feasible. In case of relabeling the existing intents, sentences of the intents may be manually annotated. In the case of splitting an existing intent into multiple intents or deleting an intent, a developer or a human agent may undergo reading all sentences of an intent pool, which may consume a lot of time.
In view of the above-mentioned problems, there appears a need to devise techniques for improving responses of automated conversational agents in an efficient and feasible manner, while precluding analysis of a huge amount of dataset or an entire intent pool.